Video-tracking of zebrafish (Danio rerio) as a biological early warning system using two distinct artificial neural networks: Probabilistic neural network (PNN) and self-organizing map (SOM)

2015 ◽  
Vol 165 ◽  
pp. 241-248 ◽  
Author(s):  
Luis Oliva Teles ◽  
Miguel Fernandes ◽  
João Amorim ◽  
Vitor Vasconcelos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Danio Rerio ◽  
Early Warning System ◽  
Probabilistic Neural Network ◽  
Warning System ◽  
Video Tracking ◽  
Self Organizing Map ◽  
Biological Early Warning System

Application of Artificial Neural Networks for Diagnosing Acute Appendicitis

2013 ◽  
Vol 479-480 ◽  
pp. 445-450
Author(s):  
Sung Yun Park ◽  
Sangjoon Lee ◽  
Jae Hoon Jeong ◽  
Sung Min Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Acute Appendicitis ◽  
Network Structure ◽  
Scoring System ◽  
Probabilistic Neural Network ◽  
Clinical Scoring ◽  
Neural Network Structure ◽  
Artificial Neural

The purpose of this study is to develop an appendicitis diagnosis system, by using artificial neural networks (ANNs). Acute appendicitis is one of the most common surgical emergencies of the abdomen. Various methods have been developed to diagnose appendicitis, but these methods have not shown good performance in the Middle East and Asia, or even in the West. We used the structures of ANNs with 801 patients. These various structures are a multilayer neural network structure (MLNN), a radial basis function neural network structure (RBF), and a probabilistic neural network structure (PNN). The Alvarado clinical scoring system was used for comparison with the ANNs. The accuracy of MLNN, RBF, PNN, and Alvarado was 97.84%, 99.80%, 99.41% and 72.19%, respectively. The AUC of MLNN, RBF, PNN, and Alvarado was 0.985, 0.998, 0.993, and 0.633, respectively. The performance of ANNs was significantly better than the Alvarado clinical scoring system (P<0.001). The models developed to diagnose appendicitis using ANNs showed good performance. We consider that the developed models can help junior clinical surgeons diagnose appendicitis.

A STUDY OF ULTRASONIC LIVER IMAGES CLASSIFICATION WITH ARTIFICIAL NEURAL NETWORKS BASED ON FRACTAL GEOMETRY AND MULTIRESOLUTION ANALYSIS

2004 ◽  
Vol 16 (02) ◽  
pp. 59-67 ◽  
Author(s):  
WEN-LI LEE ◽  
KAI-SHENG HSIEH ◽  
YUNG-CHANG CHEN ◽  
YING-CHENG CHEN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fractal Geometry ◽  
Feature Vector ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Attractive Alternative ◽  
K Nearest Neighbor ◽  
Artificial Neural

In this study, we evaluate the accuracy of classifiers for classification of ultrasonic liver tissues. Two different statistic classifiers and three various artificial neural networks are included: Bayes classifier, k-nearest neighbor classifier, Back-propagation neural networks, probabilistic neural network and modified probabilistic neural network. These five different classifiers were investigated to determine their ability to classify various categories of ultrasonic liver images. The investigation was performed on the basis of the same feature vector. For statistic classifiers the classification accuracy is at most 90.7% and with artificial neural networks the accuracy is at least 92%. The experimental results illustrated that artificial neural networks are an attractive alternative to conventional statistic techniques when dealing with classification task. Moreover, the feature vector based on fractal geometry and wavelet transform can provide good discriminant ability for ultrasonic liver images under study.

scholarly journals Texture Feature Extraction for Batik Images Using GLCM and GLRLM with Neural Network Classification

2019 ◽  
pp. 06-15
Author(s):  
K. Chandraprabha ◽  
S. Akila
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Probabilistic Neural Network ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Long Run ◽  
Short Run ◽  
Artificial Neural

Batik has a vast variety of motifs and colors. Aside from its popularity as being part of Indonesian culture, it has become the source of Indonesia’s income. Batik was more promising in the past years for the business opportunities. Batik has economic and high export value as the commodity. Batik has become the main part of national culture; however there is a lack of understanding for many people, as they are still unaware about batik motifs and patterns. Therefore, it is needed for building a model to identify batik motifs. This study aims to combine the features of texture and the feature of shapes’ ornament in batik to classify images using artificial neural networks. The value of texture features of images in batik is extracted using gray level co-occurrence matrices (GLCM) which include Contrast, Correlation, Homogeneity and Energy. And include the Gray level Run length matrices (GLRLM) which includes Gray Level Non-Uniformity (GLN), Long Run Emphasis (LRE), Short Run Emphasis (SRE), Run Percentage (RP). At this phase of the training and testing, we compare the value of a classification accuracy of neural networks in each class in batik with their texture features, and the combination of GLCM and GLRLM. From the three features used in the classification of batik image with artificial neural networks it includes Probabilistic Neural network, it was obtained that GLCM feature has the lowest accuracy rate of 78% and the combination of GLCM and GLRLM features produces a greater value of accuracy by 84%. The results obtained in this study indicate that there is an increase in accuracy of batik image classification using the probabilistic neural network with the combination of GLCM and GLRLM features in batik image.

Stress test of a biological early warning system with zebrafish (Danio rerio)

Ecotoxicology ◽  
2016 ◽  
Vol 26 (1) ◽  
pp. 13-21 ◽  
Author(s):  
João Amorim ◽  
Miguel Fernandes ◽  
Vitor Vasconcelos ◽  
Luis Oliva Teles
Keyword(s):  
Danio Rerio ◽  
Early Warning ◽  
Early Warning System ◽  
Stress Test ◽  
Warning System ◽  
Biological Early Warning System

scholarly journals Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement

2021 ◽  
Vol 11 (8) ◽  
pp. 3429
Author(s):  
Željka Beljkaš ◽  
Nikola Baša
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Research ◽  
Fiber Reinforced Polymer ◽  
Glass Fiber Reinforced Polymer ◽  
Reinforced Polymer ◽  
Continuous Beams ◽  
Glass Fiber Reinforced ◽  
Gfrp Reinforcement

Deflections on continuous beams with glass fiber-reinforced polymer (GFRP) reinforcement are calculated in accordance with the appropriate standards (ACI 440.1R-15, CSA S806-12). However, experimental research provides results which differ from the values calculated pursuant to the standards, particularly when it comes to continuous beams. Machine learning methods can be applied for predicting a deflection level on continuous beams with GFRP (glass fiber-reinforced polymer) reinforcement and loaded with a concentrated load. This paper presents research on using artificial neural networks for deflection estimation and an optimal prediction model choice. It was necessary to first develop a database, in order to train the neural network. The database was formed based on the results of the experimental research on continuous beams with GFRP reinforcement. Using the best trained neural network model, high accuracy was obtained in estimating deflection, expressed over the mean absolute percentage error, 9.0%. This result indicates a high level of reliability in the prediction of deflection with the help of artificial neural networks.

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